Biology subjects

Ciapina, L. P.

Publications and source records attributed to Ciapina, L. P..

2 recordsLinked to original sources

bioRxiv2025

In vivo effects of Cisplatin and titanium dioxide nanoparticles combined treatment

Cisplatin, the first metal-based chemotherapeutic drug, remains widely used despite its toxicity. Combining cisplatin with nanoparticles has been proposed to improve its therapeutic profile, although most studies rely on in vitro models. Using RNA-seq and bioinformatic analyses, we investigated the in vivo transcriptional effects of cisplatin (50 g/ml) and titanium dioxide nanoparticles (TiO2 NPs; 50 g/ml), alone and in combination, in Drosophila melanogaster. Flies exposed to cisplatin or TiO2 NPs alone exhibited modulation of genes associated with xenobiotic metabolism and detoxification. In contrast, combined exposure resulted in a markedly reduced number of upregulated differentially expressed genes (DEGs):CG10013, aqz, CG5568, and CG3213, which are related to cell division and DNA/RNA metabolism, likely due to a synergistic effect of the cisplatin and TiO2 administered simultaneously. We also observed that combined exposure promotes downregulation of genes involved in xenobiotic metabolism, detoxification, innate immune collapse, and reproduction and fertility. Although mortality rates were not significantly affected in any group, flies from the CIS/NPTIO2 group exhibited impaired climbing performance. These results suggest that co-exposure to cisplatin and TiO2 nanoparticles induces transcriptional suppression, potentially impairing essential cellular processes.

pharmacology and toxicology↗

bioRxiv2025

MettleRNASeq: Complex RNA-Seq Data Analysis and Gene Relationships Exploration Based on Machine Learning

Typical differential gene expression (DGE) analysis might struggle when RNA-Seq datasets possess characteristics that hinder the power of statistical analyses and the obtention of accurate conclusions, such as a limited number of replicates and high variability. We present MettleRNASeq, a robust alternative for complex RNA-Seq data analysis that integrates machine learning techniques - a tailored classification approach, association rule mining, and complementary correlation analysis - to accurately identify key genes that distinguish experimental conditions and emphasize gene relationships. This approach provides full control over critical parameters, making it versatile for transcriptomic analyses and enhancing the comprehension of disease mechanisms, treatments, and their progression. MettleRNASeq was applied for the analysis of complex radiotherapy datasets. While popular DGE tools showed an inability to accurately differentiate the distinct radiotherapy treatments, MettleRNASeq effectively and consistently indicated relevant genes for condition discrimination and identified meaningful gene relationships related to radiotherapy, highlighting condition-specific and shared gene relationships. MettleRNASeq is implemented as an R package and available on GitHub at https://github.com/SamellaSalles/MettleRNASeq.

bioinformatics↗